Every paper checked — nothing hidden

All papers

Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.

All17,313 In brief24 More (on-topic)3,333 Not relevant13,754 Not assessed87 Older352 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
Jev-Guard: Fast Routing and Auditing Agent Actions over Editable Policy Trees Introduces a low-latency guardrail model that routes incoming agent actions through editable policy trees for safety auditing. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not focus on retrieval-augmented generation techniques.
Probing the Geometry of Color Representations in Multilingual Language Models Probes layer representations in multilingual language models to study whether internal color geometry aligns with human perception or language syntax. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval or external grounding.
Probing the Geometry of Color Representations in Multilingual Language Models Probes layer representations in multilingual language models to study whether internal color geometry aligns with human perception or language syntax. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not cover model compression, efficiency, or serving.
Probing the Geometry of Color Representations in Multilingual Language Models Probes layer representations in multilingual language models to study whether internal color geometry aligns with human perception or language syntax. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Investigates text-only multilingual language models, not multimodal systems.
Probing the Geometry of Color Representations in Multilingual Language Models Probes layer representations in multilingual language models to study whether internal color geometry aligns with human perception or language syntax. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on representation probing, not benchmarking or evaluation methodology.
Probing the Geometry of Color Representations in Multilingual Language Models Probes layer representations in multilingual language models to study whether internal color geometry aligns with human perception or language syntax. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies internal representations rather than agent planning or actions.
Probing the Geometry of Color Representations in Multilingual Language Models Probes layer representations in multilingual language models to study whether internal color geometry aligns with human perception or language syntax. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Explores representation geometry rather than deployment safety or robustness.
Probing the Geometry of Color Representations in Multilingual Language Models Probes layer representations in multilingual language models to study whether internal color geometry aligns with human perception or language syntax. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not evaluate code generation or software engineering.
Pin It on Anyone: Certified Targeted Displacement of Multi-Bit LLM Watermarks Without the Key Demonstrates an attack method that alters embedded multi-bit text watermarks without knowing the secret generation key. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Does not involve multi-step agent systems or tool use.
Pin It on Anyone: Certified Targeted Displacement of Multi-Bit LLM Watermarks Without the Key Demonstrates an attack method that alters embedded multi-bit text watermarks without knowing the secret generation key. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates watermarks rather than general LLM capabilities or benchmarks.
Pin It on Anyone: Certified Targeted Displacement of Multi-Bit LLM Watermarks Without the Key Demonstrates an attack method that alters embedded multi-bit text watermarks without knowing the secret generation key. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Exclusively covers text watermarking without multimodal data.
Pin It on Anyone: Certified Targeted Displacement of Multi-Bit LLM Watermarks Without the Key Demonstrates an attack method that alters embedded multi-bit text watermarks without knowing the secret generation key. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve retrieval-augmented generation systems.
Pin It on Anyone: Certified Targeted Displacement of Multi-Bit LLM Watermarks Without the Key Demonstrates an attack method that alters embedded multi-bit text watermarks without knowing the secret generation key. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not address coding tasks or software engineering.
Pin It on Anyone: Certified Targeted Displacement of Multi-Bit LLM Watermarks Without the Key Demonstrates an attack method that alters embedded multi-bit text watermarks without knowing the secret generation key. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on model compression or inference efficiency.
Multi-Stream Residual Computation in Diffusion Language Models Equips diffusion language models with multiple residual streams and adaptive routing to separate visible context from masked token generation. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not focus on software engineering or code generation.
Multi-Stream Residual Computation in Diffusion Language Models Equips diffusion language models with multiple residual streams and adaptive routing to separate visible context from masked token generation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on efficiency, quantization, or model distillation.
Multi-Stream Residual Computation in Diffusion Language Models Equips diffusion language models with multiple residual streams and adaptive routing to separate visible context from masked token generation. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Covers text diffusion models, not multimodal models.
Multi-Stream Residual Computation in Diffusion Language Models Equips diffusion language models with multiple residual streams and adaptive routing to separate visible context from masked token generation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Investigates architecture representations, not safety or guardrails.
Multi-Stream Residual Computation in Diffusion Language Models Equips diffusion language models with multiple residual streams and adaptive routing to separate visible context from masked token generation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on internal model architecture, not agents or multi-step execution.
Multi-Stream Residual Computation in Diffusion Language Models Equips diffusion language models with multiple residual streams and adaptive routing to separate visible context from masked token generation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not propose evaluation frameworks or benchmark analyses.
Multi-Stream Residual Computation in Diffusion Language Models Equips diffusion language models with multiple residual streams and adaptive routing to separate visible context from masked token generation. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve external retrieval or knowledge grounding.
Prompt-Injection Probes Learn the Conversation, Not Just the Injection Shows linear prompt-injection monitors overfit to conversation features and reduces their false alarms by adding matched harmless negative examples. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, or efficient serving.
Prompt-Injection Probes Learn the Conversation, Not Just the Injection Shows linear prompt-injection monitors overfit to conversation features and reduces their false alarms by adding matched harmless negative examples. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on injection detection rather than retrieval-augmented generation systems.
Prompt-Injection Probes Learn the Conversation, Not Just the Injection Shows linear prompt-injection monitors overfit to conversation features and reduces their false alarms by adding matched harmless negative examples. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not focused on software engineering or coding assistants.
Prompt-Injection Probes Learn the Conversation, Not Just the Injection Shows linear prompt-injection monitors overfit to conversation features and reduces their false alarms by adding matched harmless negative examples. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on detection defense mechanisms rather than benchmark development.
Prompt-Injection Probes Learn the Conversation, Not Just the Injection Shows linear prompt-injection monitors overfit to conversation features and reduces their false alarms by adding matched harmless negative examples. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text-based prompt injection, not multimodal models.
Revisiting the Estimation of Unigram Surprisal in Computational Psycholinguistics Proposes estimating both contextual and unigram surprisal from the same language model to reduce estimation mismatch in reading time studies. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not address software engineering or code generation.
Revisiting the Estimation of Unigram Surprisal in Computational Psycholinguistics Proposes estimating both contextual and unigram surprisal from the same language model to reduce estimation mismatch in reading time studies. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, or efficient serving.
Revisiting the Estimation of Unigram Surprisal in Computational Psycholinguistics Proposes estimating both contextual and unigram surprisal from the same language model to reduce estimation mismatch in reading time studies. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on statistical psycholinguistic estimators rather than LLM benchmarking.
Revisiting the Estimation of Unigram Surprisal in Computational Psycholinguistics Proposes estimating both contextual and unigram surprisal from the same language model to reduce estimation mismatch in reading time studies. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals with text unigram surprisal, not multimodal models.
Revisiting the Estimation of Unigram Surprisal in Computational Psycholinguistics Proposes estimating both contextual and unigram surprisal from the same language model to reduce estimation mismatch in reading time studies. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies computational psycholinguistics and reading times, not LLM agents.
Revisiting the Estimation of Unigram Surprisal in Computational Psycholinguistics Proposes estimating both contextual and unigram surprisal from the same language model to reduce estimation mismatch in reading time studies. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation.
Revisiting the Estimation of Unigram Surprisal in Computational Psycholinguistics Proposes estimating both contextual and unigram surprisal from the same language model to reduce estimation mismatch in reading time studies. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address AI safety, jailbreaks, or guardrails.
Receiver Decides Agent-to-Agent Negotiations Studies agent-to-agent negotiations over money, finding that the receiving agent's characteristics primarily dictate the final transfer amounts. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not investigate model compression, distillation algorithms, or quantization.
Receiver Decides Agent-to-Agent Negotiations Studies agent-to-agent negotiations over money, finding that the receiving agent's characteristics primarily dictate the final transfer amounts. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text-based agent negotiations, not multimodal models.
Receiver Decides Agent-to-Agent Negotiations Studies agent-to-agent negotiations over money, finding that the receiving agent's characteristics primarily dictate the final transfer amounts. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve retrieval-augmented generation.
Receiver Decides Agent-to-Agent Negotiations Studies agent-to-agent negotiations over money, finding that the receiving agent's characteristics primarily dictate the final transfer amounts. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not focused on software engineering or code generation.
Reliable Memory from Unstable Growth: Curating Self-Written Agent Playbooks with Randomized Experiments Presents a randomized experiment framework to test and curate self-written agent playbooks, preventing task failure caused by unverified lessons. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression, quantization, or efficient serving.
Reliable Memory from Unstable Growth: Curating Self-Written Agent Playbooks with Randomized Experiments Presents a randomized experiment framework to test and curate self-written agent playbooks, preventing task failure caused by unverified lessons. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent textual memory, not multimodal systems.
Reliable Memory from Unstable Growth: Curating Self-Written Agent Playbooks with Randomized Experiments Presents a randomized experiment framework to test and curate self-written agent playbooks, preventing task failure caused by unverified lessons. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an agent memory curation method rather than an evaluation benchmark.
Reliable Memory from Unstable Growth: Curating Self-Written Agent Playbooks with Randomized Experiments Presents a randomized experiment framework to test and curate self-written agent playbooks, preventing task failure caused by unverified lessons. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not specifically focused on code generation or software repair.
Reliable Memory from Unstable Growth: Curating Self-Written Agent Playbooks with Randomized Experiments Presents a randomized experiment framework to test and curate self-written agent playbooks, preventing task failure caused by unverified lessons. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on persistent textual memory and playbooks for agents, not RAG.
Estimating Surprisal from the Speech Signal Estimates surprisal directly from continuous speech signals using a speech language model and importance sampling over abstract linguistic units. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not cover model compression, distillation, or efficient inference.
Estimating Surprisal from the Speech Signal Estimates surprisal directly from continuous speech signals using a speech language model and importance sampling over abstract linguistic units. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to code generation or software engineering.
Estimating Surprisal from the Speech Signal Estimates surprisal directly from continuous speech signals using a speech language model and importance sampling over abstract linguistic units. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on cognitive linguistics and speech perception, not deployment safety.
Estimating Surprisal from the Speech Signal Estimates surprisal directly from continuous speech signals using a speech language model and importance sampling over abstract linguistic units. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation systems.
Estimating Surprisal from the Speech Signal Estimates surprisal directly from continuous speech signals using a speech language model and importance sampling over abstract linguistic units. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an estimator for psycholinguistic surprisal, not LLM benchmark evaluation.
Estimating Surprisal from the Speech Signal Estimates surprisal directly from continuous speech signals using a speech language model and importance sampling over abstract linguistic units. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies psycholinguistic surprisal from speech, not autonomous agents.
Copying Is Not Grounding: Training Faithful Long-Form RAG with Authored-Sentence Credit Identifies passage copying as a failure mode in long-form retrieval systems and introduces sentence-level credit assignment to encourage genuine grounding. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression or efficient serving techniques.
Copying Is Not Grounding: Training Faithful Long-Form RAG with Authored-Sentence Credit Identifies passage copying as a failure mode in long-form retrieval systems and introduces sentence-level credit assignment to encourage genuine grounding. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not address code generation or software engineering.
Copying Is Not Grounding: Training Faithful Long-Form RAG with Authored-Sentence Credit Identifies passage copying as a failure mode in long-form retrieval systems and introduces sentence-level credit assignment to encourage genuine grounding. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on long-form RAG training objectives rather than autonomous agents.
Copying Is Not Grounding: Training Faithful Long-Form RAG with Authored-Sentence Credit Identifies passage copying as a failure mode in long-form retrieval systems and introduces sentence-level credit assignment to encourage genuine grounding. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a training method for RAG, not a general benchmark.
Copying Is Not Grounding: Training Faithful Long-Form RAG with Authored-Sentence Credit Identifies passage copying as a failure mode in long-form retrieval systems and introduces sentence-level credit assignment to encourage genuine grounding. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals with textual long-form RAG, not multimodal models.
Teacher-Guided Tool Learning for Small Language Models via Bidirectional KL Constraints Proposes a teacher-guided reinforcement learning method using bidirectional constraints to help small language models learn effective tool use. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on tool-use learning rather than retrieval-augmented generation.
Teacher-Guided Tool Learning for Small Language Models via Bidirectional KL Constraints Proposes a teacher-guided reinforcement learning method using bidirectional constraints to help small language models learn effective tool use. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on tool capability acquisition rather than AI safety or guardrails.
Teacher-Guided Tool Learning for Small Language Models via Bidirectional KL Constraints Proposes a teacher-guided reinforcement learning method using bidirectional constraints to help small language models learn effective tool use. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Does not involve multimodal or vision-language models.
Teacher-Guided Tool Learning for Small Language Models via Bidirectional KL Constraints Proposes a teacher-guided reinforcement learning method using bidirectional constraints to help small language models learn effective tool use. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a training algorithm, not an evaluation benchmark or judge framework.
Teacher-Guided Tool Learning for Small Language Models via Bidirectional KL Constraints Proposes a teacher-guided reinforcement learning method using bidirectional constraints to help small language models learn effective tool use. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Evaluates tool use on API benchmarks, not software engineering or code generation.
How Safe Is ChatGPT Health Advice? Evidence from Real User Conversations in Four Global South Countries Evaluates the safety of ChatGPT health advice using real user conversation logs across four Global South countries. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to software engineering or code generation.
How Safe Is ChatGPT Health Advice? Evidence from Real User Conversations in Four Global South Countries Evaluates the safety of ChatGPT health advice using real user conversation logs across four Global South countries. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Examines text health conversations, not multimodal models.

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